Vehicle control device

Abstract

To provide a vehicle control device that can shorten a processing time for determining a targets existing around a vehicle from sensed point group data more than before.SOLUTION: A vehicle control device according to the present invention, which comprises a distance measurement sensor that senses a peripheral point group that is a point group of feature points around an own vehicle, further comprises: storing means that stores high-precision map information including a structure; generating means that generates point group-subtraction information showing information obtained by subtracting a structure-point group showing a point group corresponding to the structure included in the high-precision map information from the peripheral point group information showing the peripheral point group sensed by the distance measurement sensor; and identifying means that identifies a candidate target to be detected that is a candidate to be detected of the own vehicle, from the point group-subtraction information.SELECTED DRAWING: Figure 3

Description

[Technical field]

[0001] The present invention relates to a vehicle control device. [Background technology]

[0002] In recent years, research has been conducted into autonomous driving, which allows a vehicle to travel without being driven by a user. In autonomous driving control, for example, a technology has been disclosed that relates to a vehicle control device that detects targets using a lidar mounted on the vehicle and identifies whether the targets are present around the vehicle based on the detected point cloud data. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2023-32069 A Summary of the Invention [Problem to be solved by the invention]

[0004] However, since the vehicle control device identifies whether or not a target is a target from point cloud data, there is a demand for reducing the processing time required for identification, and there is room for further improvement.

[0005] The object of the present invention has been made in consideration of the above-mentioned problems, and is to provide a vehicle control device that can reduce the processing time required to determine targets present around the vehicle from detected point cloud data, compared to conventional methods. [Means for solving the problem]

[0006] In order to solve the above-mentioned problems and achieve the objective, the vehicle control device of the present invention is a vehicle control device equipped with a ranging sensor that detects a surrounding point cloud, which is a point cloud of characteristic points around the vehicle, and is equipped with a memory means for storing high-precision map information including structures, a generation means for generating point cloud subtraction information indicating information obtained by subtracting a structure point cloud indicating a point cloud corresponding to structures in the high-precision map information from the surrounding point cloud information indicating the surrounding point cloud detected by the ranging sensor, and an identification means for identifying candidate detection target objects that are candidates for detection targets for the vehicle from the point cloud subtraction information.

[0007] According to this configuration, the vehicle control device identifies detection target candidate targets that are detection candidates for the vehicle itself by subtracting the point cloud derived from road structures from the surrounding point cloud detected by the distance measurement sensor. Therefore, the vehicle control device can identify only detection target candidate targets by excluding the targets derived from road structures. Therefore, the vehicle control device can reduce the processing time for determining targets that exist around the vehicle from the detected point cloud data compared to the conventional method.

[0008] Furthermore, when the detection target candidate target is located outside the road and the size of the detection target candidate target is smaller than a predetermined size, the specification means specifies the detection target candidate target as a detection target target to be detected by the vehicle. Furthermore, when the detection target candidate target is located within the road and the size of the detection target candidate target is larger than a predetermined size, the specification means specifies the detection target candidate target as a detection target target if the position of the detection target candidate target changes after a predetermined time has passed, and excludes the detection target candidate target from the detection target targets if the position of the detection target candidate target does not change even after the predetermined time has passed. The predetermined size includes at least one of the size of a pedestrian or the size of a bicycle.

[0009] This allows the vehicle control device to identify the target object based on the positional relationship with the road and the predetermined size of the identified target object. Furthermore, if the position of the identified target object does not change, the vehicle control device can identify the target object as a stationary target. Therefore, the vehicle control device can reduce erroneous detection of road structures as target objects. Effect of the Invention

[0010] According to the present invention, it is possible to reduce the processing time required to determine targets present around a vehicle from detected point cloud data, as compared with the conventional technology. [Brief description of the drawings]

[0011] [Figure 1] FIG. 1 is a block diagram showing an example of a system configuration of a vehicle equipped with a vehicle control device according to an embodiment. [Diagram 2] FIG. 2 is a block diagram illustrating an example of a functional configuration of an autonomous driving ECU of a vehicle according to an embodiment. [Diagram 3] FIG. 3 is a block diagram illustrating an example of a functional configuration of an autonomous driving ECU of a vehicle according to an embodiment. [Figure 4] FIG. 4 is a flowchart showing an example of the flow of operation of the autonomous driving ECU of the vehicle according to the embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. The configuration of the embodiment described below and the actions and effects brought about by the configuration are merely examples, and the present invention is not limited to the following description.

[0013] 1 is a block diagram showing an example of a system configuration of a vehicle 1 equipped with a vehicle control device according to an embodiment. The vehicle 1 is equipped with an automatic driving function, and is capable of traveling by automatic driving without the driving operation of a user (driver). Note that automatic driving includes semi-automatic driving in which some of the operations for traveling of the vehicle 1 are automated (requiring partial driving operation by the user).

[0014] A plurality of ECUs (Electronic Control Units) are mounted on the vehicle 1 to control various parts. Each ECU has a microcontroller unit (microcomputer), and the microcomputer has built-in, for example, a CPU (Central Processing Unit), a non-volatile memory such as a flash memory, and a volatile memory such as a DRAM (Dynamic Random Access Memory).

[0015] The multiple ECUs include a drive ECU 11, a steering ECU 12, a brake ECU 13, a meter ECU 14, and a body ECU 15. The drive ECU 11, the steering ECU 12, the brake ECU 13, the meter ECU 14, and the body ECU 15 are connected to each other so as to be capable of communication according to a CAN (Controller Area Network) communication protocol, that is, CAN communication.

[0016] The drive ECU 11 is a control unit that controls a drive device 21 of the vehicle 1. The drive device 21 may be configured to include an engine as a drive source, a motor as a drive source, or both an engine and a motor as drive sources. The drive device 21 includes a transmission that changes the speed of the drive force from the drive source and outputs it as necessary.

[0017] The steering ECU 12 is a control unit that controls the steering device 22 of the vehicle 1. The steering device 22 is, for example, an electric power steering device that applies the torque of an electric motor to a steering mechanism. The steering mechanism includes, for example, a rack-and-pinion steering gear, and is configured such that when a rack shaft moves in the vehicle width direction due to the torque of the electric motor, the left and right steered wheels are turned left and right in accordance with the movement of the rack shaft.

[0018] The brake ECU 13 is a control unit that controls the braking device 23 of the vehicle 1. The braking device 23 may be of a hydraulic or electric type. The hydraulic braking device 23 includes a brake actuator, and the function of this brake actuator distributes hydraulic pressure to wheel cylinders of the brakes provided on each wheel, and the hydraulic pressure drives the brakes to the driving wheels. A braking force is applied to the wheels including the

[0019] The meter ECU 14 is a control unit that controls each part of a meter panel (not shown) of the vehicle 1. The meter panel is provided with indicators such as a liquid crystal display for displaying various information, in addition to instruments for displaying the vehicle speed and engine RPM. An emergency stop switch 24 that is operated to issue an instruction to emergency stop the autonomous driving is also connected to the meter ECU 14.

[0020] The body ECU 15 is a control unit that controls various parts that need to operate even when the ignition switch of the vehicle 1 is in the off state, such as the left and right turn signals and the door lock motors.

[0021] The plurality of ECUs also include an autonomous driving ECU 31, a lidar ECU 32, and a monocular camera ECU 33 as control units for the autonomous driving function.

[0022] The automatic driving ECU 31 is a control center for automatic driving control. The automatic driving ECU 31 is an example of a vehicle control device. The automatic driving ECU 31 is connected to the drive ECU 11, the steering ECU 12, the brake ECU 13, the meter ECU 14, and the body ECU 15 so as to be able to communicate with them via CAN.

[0023] An omnidirectional lidar (LiDAR: Light Detection And Ranging) 34 is connected to the autonomous driving ECU 31 via, for example, a communication cable conforming to the Ethernet (registered trademark) standard. The omnidirectional lidar 34 is an example of a "distance measurement sensor" capable of acquiring distance measurement information indicating the distance from the vehicle 1 to an object present around the vehicle 1. The omnidirectional lidar 34 irradiates laser light in all directions of 360°, receives reflected light from an object present within a search range with an optical sensor, and outputs a detection signal according to the reflected light as distance measurement information. The distance measurement information may take the form of, for example, point cloud information indicating the distance from the vehicle 1 to the object at each position (voxel) in a three-dimensional space. The detection signal of the omnidirectional lidar 34 is input to the autonomous driving ECU 31.

[0024] Further, the automatic driving ECU 31 is connected to a GPS receiver 35 via, for example, a communication cable conforming to the USB (Universal Serial Bus) standard. The GPS receiver 35 is a receiver that receives positioning signals from GPS (Global Positioning System) satellites. The GPS receiver 35 is an example of a "positioning sensor" that can acquire positioning information indicating a position on the earth (e.g., latitude, longitude, altitude, etc.). The positioning signal received by the GPS receiver 35 is input from the GPS receiver 35 to the automatic driving ECU 31 as positioning information.

[0025] The LIDAR ECU 32 is connected to the autonomous driving ECU 31 so as to be able to communicate with it via, for example, an Ethernet standard communication cable. Six LIDARs 36 are connected to the LIDAR ECU 32. The LIDAR 36 irradiates a search range with a laser light, receives reflected light from an object present within the search range with an optical sensor, and outputs a detection signal according to the reflected light. The LIDARs 36 are arranged, for example, at the left end, center, and right end of the front bumper of the vehicle 1 and the left end, center, and right end of the rear bumper. The LIDARs 36 are an example of a distance measuring sensor. The LIDAR ECU 32 receives a detection signal output from each LIDAR 36. The LIDAR ECU 32 processes the detection signal output from each LIDAR 36 and transmits data obtained by the processing to the autonomous driving ECU 31.

[0026] The monocular camera ECU 33 is communicatively connected to the autonomous driving ECU 31 via, for example, a USB standard communication cable. A monocular camera 37 is connected to the monocular camera ECU 33. The monocular camera 37 is a camera capable of continuously capturing still images of a search range in front of the vehicle 1 at a predetermined frame rate. An image signal of a still image continuously output from the monocular camera 37 is input to the monocular camera ECU 33. The monocular camera ECU 33 processes the image signal input from the monocular camera 37, and transmits image data obtained by the processing to the autonomous driving ECU 31.

[0027] Fig. 2 is a block diagram showing an example of a functional configuration of the autonomous driving ECU 31 of the vehicle 1 according to the embodiment. The autonomous driving ECU 31 of the present embodiment includes an object recognition unit 41, a self-position estimation unit 42, a surrounding information integration unit 43, a route planning unit 44, and a vehicle control unit 45. These functional units 41 to 45 are configured by cooperation between hardware and software (programs, etc.) constituting the vehicle 1 as exemplified in Fig. 1. At least one of these functional units 41 to 45 may be configured by dedicated hardware (circuits, etc.). Note that the functional configuration of the autonomous driving ECU 31 is not limited to this.

[0028] The object recognition unit 41 recognizes targets such as other vehicles and pedestrians around the vehicle 1 from information on the distance to targets (vehicles, pedestrians, buildings, curbs, and other obstacles) acquired from the detection signals of the omnidirectional lidar 34 and from images captured by the monocular camera 37. The detection signals are also referred to as point cloud data.

[0029] For example, the object recognition unit 41 recognizes targets by performing the following processes. Specifically, the object recognition unit 41 performs a ground removal process to remove point clouds due to reflection from the ground from the point cloud data of the omnidirectional lidar 34. The object recognition unit 41 also performs a clustering process to group the point clouds into points that are close to each other. Furthermore, the object recognition unit 41 performs a boxing process to fit the grouped point clouds into a rectangular parallelepiped shape in order to recognize them as target objects to be detected that exist around the vehicle.

[0030] Then, the object recognition unit 41 performs a tracking process to calculate the relative speed of the target object by monitoring the boxed rectangular parallelepiped shape. This allows the object recognition unit 41 to recognize the target object. Note that the boxed information that fits the grouped point cloud into the rectangular parallelepiped shape is also called surrounding point cloud information that indicates the surrounding point cloud.

[0031] The self-position estimation unit 42 estimates the position (self-position) of the vehicle 1 by matching the point cloud data acquired from the detection signal of the omnidirectional LIDAR 34 with high-precision map data (point cloud data) 46, which is data of a high-precision map. The high-precision map is a high-precision three-dimensional map, and the high-precision map data 46 includes information such as road width and gradient, and information on features such as dividing lines, shoulder lines, intersections, railroad crossings, stop lines, pedestrian crossings, and signs. In other words, the high-precision map is high-precision map information including structures derived from road structures and point clouds of structures derived from road structures.

[0032] The high-precision map data 46 may be stored in a non-volatile memory built into the microcomputer of the automatic driving ECU 31, or may be stored in an HDD (Hard Disk Drive) or the like connected to the automatic driving ECU 31. Furthermore, the self-position estimation unit 42 integrates the self-position estimated by matching the point cloud data with the self-position based on the positioning signal received by the GPS receiver 35 to improve the estimation accuracy of the self-position.

[0033] The surrounding information integrating unit 43 receives as input the target recognition result by the object recognition unit 41, the self-position estimation result by the self-position estimation unit 42, and data obtained by processing detection signals output from each LIDAR 36 by the LIDAR ECU 32 (see FIG. 1). High-precision map data 46 is also input to the surrounding information integrating unit 43. The surrounding information integrating unit 43 creates surrounding information integrated map data in which targets such as the vehicle 1, vehicles other than the vehicle 1, and pedestrians are arranged on a high-precision map. The surrounding information integrating unit 43 then outputs the map information and object recognition information to an HMI device 47 (Human Machine Interface) such as a display arranged in the interior of the vehicle 1.

[0034] The route planning unit 44 receives the peripheral information integrated map data from the peripheral information integrating unit 43. The route planning unit 44 plans a travel route to the destination of the vehicle 1 from the input peripheral information integrated map data. The travel route includes the course of the vehicle 1 and a target vehicle speed at each point on the course. The route planning unit 44 outputs route data of the planned travel route to the HMI device 47.

[0035] The vehicle control unit 45 receives route data from the route planning unit 44. Based on the route data, the vehicle control unit 45 outputs commands to ECUs that control the operation of each part of the vehicle 1, such as the drive ECU 11, the steering ECU 12, and the brake ECU 13, so that the vehicle 1 travels by automatic driving along the travel route.

[0036] For example, after the destination of vehicle 1 is input on HMI device 47, an automatic driving start button displayed on HMI device 47 is pressed, whereby an instruction to start automatic driving is input from HMI device 47 to automatic driving ECU 31. When an instruction to start automatic driving is input to automatic driving ECU 31, a travel route to the destination of vehicle 1 is planned by route planner 44. The travel route is re-planned at a predetermined cycle while vehicle 1 is traveling by automatic driving.

[0037] Then, the vehicle 1 travels along the most recently planned travel route at the target vehicle speed for each point on the course included in the travel route. The autonomous driving ends, for example, when the vehicle 1 arrives at the destination or the emergency stop switch 24 is pressed and an instruction to stop the autonomous driving is input from the meter ECU 14 to the autonomous driving ECU 31.

[0038] However, since the autonomous driving ECU 31 determines whether an object is a target from point cloud data, there is a demand for reducing the processing time required for the determination, and there is room for further improvement. Therefore, the autonomous driving ECU 31 of this embodiment has the functions shown in FIG.

[0039] 3 is a block diagram showing an example of a functional configuration of the automatic driving ECU 31 according to the embodiment. For example, the surrounding information integration unit 43 included in the automatic driving ECU 31 includes a storage means 431, a generation means 432, an identification means 433, a first determination means 434, a second determination means 435, and a third determination means 436. Note that the functions included in the surrounding information integration unit 43 are not limited to these. In addition, in this embodiment, these functional units 431 to 436 are described as being included in the surrounding information integration unit 43, but other functional units of the automatic driving ECU 31 may include the functional units 431 to 436.

[0040] The storage means 431 stores high-precision map information including structures. Specifically, the storage means 431 stores high-precision map data 46 input to the surrounding information integrating unit 43. The storage means 431 also stores point cloud subtraction information generated by a generating means 432 described later. The storage means 431 also stores information on detection target candidate targets identified by an identifying means 433 described later. The storage means 431 stores information on detection target targets identified by the identifying means 433.

[0041] The generating means 432 generates point cloud subtraction information indicating information obtained by subtracting point cloud information indicating a point cloud corresponding to a structure in the high-precision map information from surrounding point cloud information indicating a detected surrounding point cloud. Specifically, the generating means 432 generates point cloud subtraction information indicating information obtained by subtracting point cloud information indicating a point cloud corresponding to a structure in the high-precision map information from surrounding point cloud information indicating a surrounding point cloud detected by the object recognition unit 41. As described above, the high-precision map information includes structures originating from road structures and point clouds of structures originating from road structures. The processing performed by the generating means 432 is processing for subtracting the point cloud of structures originating from road structures from the surrounding point cloud information using altitude map information, and is processing for reducing processing time.

[0042] The identification means 433 identifies a detection target candidate target that is a detection target candidate of the host vehicle from the point cloud subtraction information. Specifically, the identification means 433 identifies a target candidate target that is a detection target candidate of the host vehicle from the point cloud subtraction information generated by the generation means 432. Here, the detection target candidate target is a target that is a candidate that may be an obstacle existing around the host vehicle.

[0043] The first determination means 434 determines whether the detection target candidate object exists outside the road. Specifically, the first determination means 434 determines whether the detection target candidate object identified by the identification means 433 exists outside the road. The process performed by the first determination means 434 is a process for determining whether the detection target candidate object exists on a road on the route of the host vehicle, and is a process for reducing erroneous detection. In addition, the identification means 433 identifies the detection target candidate object, which is determined by the first determination means 434 to exist on the road, as a detection target object to be detected by the host vehicle.

[0044] The second determination means 435 determines whether the size of the detection target candidate object is larger than a predetermined size. Specifically, the second determination means 435 determines whether the size of the detection target candidate object that exists outside the road determined by the first determination means 434 is larger than a predetermined size. The predetermined size includes at least one of the size of a pedestrian or the size of a bicycle.

[0045] The process performed by the second determination means 435 is a process for determining whether the detection target candidate object is a moving object including a pedestrian, a bicycle, etc., and is a process for reducing erroneous detection. The process performed by the second determination means 435 is also a process for determining whether the detection target candidate object is a road structure, and is a process for reducing erroneous detection. In addition, the identification means 433 identifies the detection target candidate object that is determined by the second determination means 435 to be smaller than a predetermined size as a detection target object to be detected by the host vehicle.

[0046] The third determination means 436 determines whether the detection target candidate object is moving. Specifically, the third determination means 436 determines whether the detection target candidate object is moving based on the change in position after a predetermined time has elapsed. The predetermined time is, for example, a time corresponding to a period in which the detection signal of the omnidirectional lidar 34 is output. The predetermined time may be, for example, five periods, but is not limited thereto. The process performed by the third determination means 436 is a process for determining whether the detection target candidate object is a vehicle parked on the side of the road, and is a process for reducing erroneous detection.

[0047] For example, the third determination means 436 determines that the target candidate object is moving when the position of the target candidate object changes after a predetermined time has elapsed. Then, the identification means 433 identifies the target candidate object determined by the third determination means 436 to be moving as a target object to be detected by the vehicle.

[0048] Also, for example, the third determination means 436 determines that the detection target candidate object has not moved if the position of the detection target candidate object has not changed even after a predetermined time has elapsed. Then, the identification means 433 excludes the detection target object determined by the third determination means 436 as not having moved from the detection target objects.

[0049] 4 is a flowchart showing an example of the flow of the operation of the autonomous driving ECU 31 of the vehicle 1 according to the embodiment. In particular, the flow of the process performed by the surrounding information integration unit 43 will be described here.

[0050] The storage means 431 stores high-precision map information including structures (step S1). Next, the generation means 432 generates point cloud subtraction information indicating information obtained by subtracting point cloud information indicating a point cloud corresponding to the structure of the high-precision map information from surrounding point cloud information indicating the detected surrounding point cloud (step S2). Next, the identification means 433 identifies detection target candidate targets that are detection target candidates of the host vehicle from the point cloud subtraction information (step S3).

[0051] Next, the first determination means 434 determines whether the detection target candidate object is outside the road (step S4). If the first determination means 434 determines that the detection target candidate object is inside the road (step S4: No), the process proceeds to step S8. On the other hand, if the first determination means 434 determines that the detection target candidate object is outside the road (step S4: Yes), the process proceeds to step S5.

[0052] In step S5, the second determination means 435 determines whether the size of the detection target candidate object is larger than a predetermined size (step S5). If the second determination means 435 determines that the size of the detection target candidate object is smaller than the predetermined size (step S5: No), the process proceeds to step S8. On the other hand, if the second determination means 435 determines that the size of the detection target candidate object is larger than the predetermined size (step S5: Yes), the process proceeds to step S6.

[0053] In step S6, the third determination means 436 determines whether the detection target candidate object is moving (step S6). If the third determination means 436 determines that the detection target candidate object is moving (step S6: Yes), the process proceeds to step S8. On the other hand, if the third determination means 436 determines that the detection target candidate object is not moving (step S6: No), the process proceeds to step S7.

[0054] In step S7, the identification means 433 excludes the detection target candidate target that is determined not to be moving from the detection target targets (step S7). In step S8, the identification means 433 identifies the detection target candidate target as a detection target target to be detected by the host vehicle (step S8). When this process ends, the surrounding information integrating unit 43 creates surrounding information integrated map data in which targets such as the vehicle 1, vehicles other than the vehicle 1, and pedestrians are arranged on a high-precision map.

[0055] As described above, according to the autonomous driving ECU 10 of the vehicle 1 in this embodiment, high-precision map information including structures is stored, and point cloud subtraction information is generated that indicates information obtained by subtracting a structure point cloud indicating a point cloud corresponding to a structure in the high-precision map information from surrounding point cloud information indicating the surrounding point cloud detected by the ranging sensor, and candidate detection target objects that are candidates for detection targets of the host vehicle are identified from the point cloud subtraction information.

[0056] As a result, the autonomous driving ECU 10 of the vehicle 1 according to this embodiment identifies detection target candidate targets that are detection candidates for the vehicle itself by subtracting the point cloud derived from road structures from the surrounding point cloud detected by the distance measurement sensor. Therefore, the autonomous driving ECU 10 of the vehicle 1 can identify only detection target candidate targets by excluding the targets derived from road structures. Therefore, the autonomous driving ECU 10 of the vehicle 1 can reduce the processing time for determining targets present around the vehicle 1 from the detected point cloud data compared to the conventional method.

[0057] Furthermore, when a detection target candidate target is located outside the road and the size of the detection target candidate target is smaller than a predetermined size, the autonomous driving ECU 10 of the vehicle 1 specifies the detection target candidate target as a detection target target to be detected by the vehicle itself. Furthermore, when a detection target candidate target is located within the road and the size of the detection target candidate target is larger than a predetermined size, the autonomous driving ECU 10 of the vehicle 1 specifies the detection target candidate target as a detection target target if the position of the detection target candidate target changes after a predetermined time has passed, and excludes the detection target candidate target from the detection target targets if the position of the detection target candidate target does not change even after the predetermined time has passed. The predetermined size includes at least one of the size of a pedestrian or the size of a bicycle.

[0058] This allows the vehicle control device to identify the target object based on the positional relationship with the road and the predetermined size of the identified target object. Furthermore, if the position of the identified target object does not change, the vehicle control device can identify the target object as a stationary target. Therefore, the vehicle control device can reduce erroneous detection of road structures as target objects.

[0059] The program that causes a computer (e.g., the autonomous driving ECU 31, etc.) to execute processes for implementing various functions in the control device of the vehicle 1 as described above can be provided by recording it in an installable or executable format on a computer-readable recording medium such as a CD (Compact Disc)-ROM, a flexible disk (FD), a CD-R (Recordable), or a DVD (Digital Versatile Disk). The program may also be provided or distributed via a network such as the Internet. The program may also be provided by being pre-installed in a ROM, etc.

[0060] Although the embodiment of the present invention has been described above, the above-mentioned embodiment is presented as an example and is not intended to limit the scope of the present invention. This new embodiment can be implemented in various other forms. In addition, various omissions, substitutions, and modifications can be made without departing from the gist of the invention. In addition, this embodiment is included in the scope and gist of the invention, and is included in the scope of the invention and its equivalents described in the claims. [Explanation of symbols]

[0061] 1...vehicle, 11...drive ECU, 12...steering ECU, 13...brake ECU, 14...Meter ECU, 15...Body ECU, 24...Emergency stop switch, 31...Autonomous driving ECU, 32...LIDAR ECU, 33...Monocular camera ECU, 34 ... omnidirectional lidar, 35 ... GPS receiver, 36 ... lidar, 37 ... monocular camera, 41: object recognition unit, 42: self-location estimation unit, 43: surrounding information integration unit, 44: route planning unit, 45: vehicle control unit, 431: storage means, 432: generation means, 433...identifying means, 434...first determining means, 435...second determining means, 436…Third determination means

Claims

1. A vehicle control device including a distance measuring sensor that detects a surrounding point cloud, which is a point cloud of characteristic points around a vehicle, A storage means for storing high-precision map information including structures; a generating means for generating point cloud subtraction information indicating information obtained by subtracting a structure point cloud indicating a point cloud corresponding to a structure of the high-precision map information from surrounding point cloud information indicating the surrounding point cloud detected by the distance measuring sensor; A detection target candidate identifying means for identifying a detection target candidate of the host vehicle from the point cloud subtraction information; A vehicle control device comprising:

2. the identification means, when the detection target candidate object is located outside a road and the size of the detection target candidate object is smaller than a predetermined size, identifies the detection target candidate object as a detection target object to be detected by the host vehicle; The vehicle control device according to claim 1.

3. When the detection target candidate object is located within a road and the size of the detection target candidate object is larger than the predetermined size, the identification means When a position of the detection target candidate object changes after a predetermined time has elapsed, the detection target candidate object is identified as the detection target object; If the position of the detection target candidate object does not change even after a predetermined time has elapsed, the detection target candidate object is excluded from the detection target objects. The vehicle control device according to claim 2.

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